This invention relates to a method of processing images of welds, and particularly to a method of processing images of welds to detect weld defects in pipes or the like in a power plant facility.
Large machines, plant facilities, etc. include many parts connected by welded portions. These welded portions are often important sections of the facilities.
Welded portions are crucial not only for maintaining the inherent function of a machine, plant facility, etc., but also for maintaining the safety of the machine or facility and of the surrounding environment. Accordingly, welded joints are crucial points of construction, and their maintenance during operation is extremely important.
Large-scale facilities such as power plants and transformer stations, which have strict safety requirements because of their public role, undergo periodic and special maintenance including part replacement, inspection, and tuning under the laws and regulations for electric facilities. When the inspection of internal defect of portions cannot be done in a visual manner or by means of a surface inspection device, a nondestructive inspection method in the form of an X-ray transmission test specified by JIS standards and other standards is frequently employed. The standards also cover the threshold of judgement of a weld defect based on a radiographic test.
However, extremely high proficiency is required of an inspector to make a visual judgement, based on his experience, of an image of a weld defect formed by radiography, so the result of judgement is often lacking in reliability.
Visual inspection and judgement by a skilled inspector rely on a threshold of judgement which can differ depending on the person and can also vary due to visual fatigue and psychological pressure. Therefore, it is difficult to make a stable and objective judgement.
Unless a definite judgement concerning a weld defect is made with high reliability, a further step of maintenance activity cannot be taken, and this situation can impose a serious adverse impact on the operation of a facility and also on its safety.
Therefore, it is extremely desirable for a power plant to be able to automatically evaluate weld defects based on radiography.
A method of automatic defect extraction has been proposed in which an image of a welded joint of a pipe or the like is formed on radiographic film and the developed image is introduced into a computer so that a statistic process can be performed on the image, and the result of the process is used for judgement by an inspector.
An example of a defect extraction method is to categorize defects through the emphasis of defect image by selective application of a computational operator, binary conversion of the emphasized image using a certain threshold, and determination based on the density of the image. This method will be called "the first method" hereinafter.
Another known method is based on the spatial frequency of the density distribution normal to a welding line, in which spectral components of a radiographic image are fed through a band-pass filter to extract a discontinuity at a defective portion. The filter output is subjected to binary conversion based on a certain threshold to identify weld defects. This method will be called "the second method" hereinafter.
A further method employs a signal which is produced by scanning a joint in the direction normal to a welding line. The difference between the signal and its quadratic approximation is subjected to a high-frequency filtering process to emphasize the radiographic image to be subjected to a maximizing process. This method will be called "the third method" hereinafter.
Another known method makes a quadratic curve approximation of the brightness distribution of a radiographic image taken in the direction normal to a welding line and the approximated curve is subtracted from the original curve to produce an emphasized differential image. The image is subjected to binary conversion based on a threshold value derived from the density difference, with the image being modified to clarify the boundary of binary image regions. This method will be called "the fourth method" hereinafter.
In order to infer the type of a weld defect from a radiographic image, a method has been proposed for deterministically classifying the type of weld defect by specifying the existing range of features for each defect type, on the basis of features of the shape of the defect image obtained by image processing of a radiographic image. Also, a method for inferring the type of weld defect from the statistic distribution of features using probability has been proposed. These methods will be called "the fifth method" hereinafter. Examples of this inspection system based on Bayes' law are described in the Proceedings of the Fourth Industrial Image Sensing Technology Symposium, pp. 51-56, entitled "WELD INSPECTION EXPERT SYSTEM WITH IMAGE PROCESSING ENVIRONMENT" by Koshimizu et al. and in the publication Transactions of JWRI, Vol. 11, No. 2, 1982, pp. 123-132, entitled "Automatic Recognition of Weld Defects in Radiographic Test (Report 1)", by Inoue et al.
However, while these conventional methods of extracting a weld defect by an inspector each have advantages, they are impractical for the following reasons.
The first method has the problem that it is difficult to select an operator, i.e., the selection of an operator is not possible unless the image of the defect is known in advance. Since the pixel values of the emphasized image do not have a direct physical meaning, threshold setting must be performed by trial and error. Therefore, there is a high possibility of distortion of an emphasized defect image used for judging defects. Despite the fact that the extracted defect image can possibly include irrelevant indications, this method lacks a processing scheme for eliminating such indications, resulting possibly in oversensitive defect detection.
The second method is capable of extracting a volumetric weld defect such as a blow hole, but it has difficulty extracting a defect image with a small density difference such as a lack of fusion. Since the defect image is discriminated based solely on the extracted density difference, the result of detection is not consistent with that of an experienced human inspector. Therefore, this method is lacking in practicality as an extraction method, and it has the drawback that it is difficult to determine the threshold value.
The third method is deficient in that the defect image cannot be emphasized due to a small density difference for a planar weld defect such as a lack of fusion, as in the second method. Although this method produces a sharp image, it cannot eliminate the density variation at a residual weld portion, and it cannot perform defect image extraction with a single threshold value. The third method by itself is not intended for the automatic extraction of a defect image.
The fourth method is incapable of extracting a defect image with a small density difference such as a lack of fusion. Due to its defect image extraction being based solely on the density difference among pixels of an emphasized image, the sensitivity of detection can differ from that of an experienced inspector. Consequently, it has poor reliability and is inconsistent with the result provided by an inspector because its modification process is based solely on a binary-converted image.
The fifth method proposed by Koshimizu et al. and Inoue et al. involves the following problems. The inspection system proposed by Koshimizu et al. is a determinative method based on a logical sequence, and it is deficient with respect to the addition of new judgement rules and with respect to logical treatment. Although this method is advantageous in the logical processing and measured data processing using graphical data of a weld defect portion and the positions of density around the defective portion, the knowledge of an experienced inspector cannot be introduced or added to the expert system. The system proposed by Inoue et al. uses Bayes' law, and it requires complete data collection. For example, the system needs nine features of a defect, and it does not allow merging with an expert system.